Diffusion-weighted MRI radiomics of spine bone tumors: feature stability and machine learning-based classification performance.

ABSTRACT:

Purpose

To evaluate stability and machine learning-based classification performance of radiomic features of spine bone tumors using diffusion- and T2-weighted magnetic resonance imaging (MRI).

Material and methods

This retrospective study included 101 patients with histology-proven spine bone tumor (22 benign; 38 primary malignant; 41 metastatic). All tumor volumes were manually segmented on morphologic T2-weighted sequences. The same region of interest (ROI) was used to perform radiomic analysis on ADC map. A total of 1702 radiomic features was considered. Feature stability was assessed through small geometrical transformations of the ROIs mimicking multiple manual delineations. Intraclass correlation coefficient (ICC) quantified feature stability. Feature selection consist

SUBMITTER: Gitto S 

PROVIDER: S-EPMC9098537 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature